code: def avoid_overflow(self, observation, reset_action=None):
      if reset_action is None:
          (
              obs_simulate,
              done_simulate,
              to_check_action,
              to_maintain_lines,
          ) = self.simulate_do_nothing(observation)
      else:
          to_check_action = reset_action
          to_maintain_lines = []
          (
              obs_simulate,
              reward_simulate,
              done_simulate,
              info_simulate,
          ) = observation.simulate(to_check_action)
          observation._obs_env._reset_to_orig_state()
  
      has_overflow = False
      if observation is not None and not any(np.isnan(observation.rho)):
          has_overflow = any(observation.rho > 1.0) or any(
              obs_simulate.rho > 1.0
          )
  
      if not (done_simulate or has_overflow) and (
          to_check_action == self.do_nothing_action
      ):
          return self.do_nothing_action, -1
      if (
          to_check_action != self.do_nothing_action
          and obs_simulate.rho.max() < 1.0
          and not done_simulate
      ):
          return to_check_action, -1
  
      # action selection and rerank
      top_idx, pred_rho = self._predict_rho(observation)
      action_selected = [False] * len(self.actions)
      for i in range(80):
          idx = top_idx[i]
          action_selected[idx] = True
  
      # select_action_by_dis
      overflow_lines = np.where(observation.rho > 1.0)[0].tolist()
      if len(overflow_lines) == 0:
          overflow_lines = np.where(obs_simulate.rho > 1.0)[0].tolist()
  
      best_idx = -1
      least_overflow_action = self.do_nothing_action
      least_overflow = 10.0
      least_obs_simulate = obs_simulate
      if obs_simulate is not None and not any(
          np.isnan(obs_simulate.rho)
      ):
          least_overflow = float(np.max(obs_simulate.rho))
  
      if reset_action is None:
          illegal_pos = self.find_unaccessible_pos(to_check_action)
      else:
          illegal_pos = []
  
      self.simulation_times += 1
      found = False
      for idx in range(self.act_num):
          if not action_selected[idx]:
              continue
          to_simulate_action = self.actions[idx]
          # check conflict
          if to_check_action != self.do_nothing_action:
              illegal_pos_value = to_simulate_action._set_topo_vect[
                  illegal_pos
              ]
              if np.any(illegal_pos_value):
                  continue
              action1_vec = to_simulate_action.to_vect()
              action2_vec = to_check_action.to_vect()
              to_simulate_action = self.action_space.from_vect(
                  action1_vec + action2_vec
              )
          legal_action = self.correct_action(
              observation, to_simulate_action, self.sub_ids[idx]
          )
          if legal_action == self.do_nothing_action:
              continue
  
          (
              obs_simulate,
              reward_simulate,
              done_simulate,
              info_simulate,
          ) = observation.simulate(legal_action)
          observation._obs_env._reset_to_orig_state()
          max_rho = obs_simulate.rho.max()
  
          assert (
              not info_simulate["is_illegal"]
              and not info_simulate["is_ambiguous"]
          )
  
          if obs_simulate is not None and not any(
              np.isnan(obs_simulate.rho)
          ):
              if not done_simulate:
                  overflow_value = float(np.max(obs_simulate.rho))
                  if (not found) and (overflow_value < least_overflow):
                      least_overflow = overflow_value
                      least_overflow_action = legal_action
                      least_obs_simulate = obs_simulate
                      best_idx = idx
                  if least_overflow < 0.95:
                      if not found:
                          pass
                      found = True
                      break
                  continue
  
      if best_idx != -1:
          least_overflow_action = self.correct_action(
              observation, least_overflow_action, self.sub_ids[best_idx]
          )
          if (
              to_check_action != self.do_nothing_action
              and least_overflow_action != self.do_nothing_action
              and reset_action is None
          ):
              self.last_disconnect_step = self.step - 1
              self.last_diconnect_line = to_maintain_lines[0]
          if reset_action is not None:
              pass
          return least_overflow_action, self.sub_ids[best_idx]
      else:
          return self.do_nothing_action, -1
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/rl_agent.py
imports: import os
  import numpy
  from copy import deepcopy
  from utils import process
  from powernet_model import PowerNetModel
  from grid2op.Agent import BaseAgent
  import torch
label_desc: Use image node_id 7 for calling the RLAgent obj's underlying member method code with example usage: obj.avoid_overflow(observation, reset_action) and returns: self,  unknown, to_check_action,  unknown, least_overflow_action,  self, self,  unknown